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Found 42 papers, 3 papers with code
Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
This work introduces a unique approach combining Game Theory (GT) and Deep Reinforcement Learning (DRL) for optimizing the distribution of AI inference workloads in geo-distributed data centers to reduce carbon emissions and cloud operating (energy + data transfer) costs.
Silicon Photonic 2.5D Interposer Networks for Overcoming Communication Bottlenecks in Scale-out Machine Learning Hardware Accelerators
Modern machine learning (ML) applications are becoming increasingly complex and monolithic (single chip) accelerator architectures cannot keep up with their energy efficiency and throughput demands.
SANGRIA: Stacked Autoencoder Neural Networks with Gradient Boosting for Indoor Localization
Indoor localization is a critical task in many embedded applications, such as asset tracking, emergency response, and realtime navigation.
Accelerating Neural Networks for Large Language Models and Graph Processing with Silicon Photonics
In the rapidly evolving landscape of artificial intelligence, large language models (LLMs) and graph processing have emerged as transformative technologies for natural language processing (NLP), computer vision, and graph-structured data applications.
STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization
However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy.
MOSAIC: A Multi-Objective Optimization Framework for Sustainable Datacenter Management
To co-optimize the energy cost, carbon emissions, and water footprint of datacenter operation from a global perspective, we propose a novel framework for multi-objective sustainable datacenter management (MOSAIC) that integrates adaptive local search with a collaborative decomposition-based evolutionary algorithm to intelligently manage geographical workload distribution and datacenter operations.
CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization
Indoor localization has become increasingly vital for many applications from tracking assets to delivering personalized services.
SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon, Wastewater, and Energy-Aware Data Center Management
Today's cloud data centers are often distributed geographically to provide robust data services.
Analysis of Optical Loss and Crosstalk Noise in MZI-based Coherent Photonic Neural Networks
The proposed models can be applied to any SP-NN architecture with different configurations to analyze the effect of loss and crosstalk.
GHOST: A Graph Neural Network Accelerator using Silicon Photonics
Graph neural networks (GNNs) have emerged as a powerful approach for modelling and learning from graph-structured data.
FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices
By deploying machine learning (ML) based indoor localization frameworks on their mobile devices, users can localize themselves in a variety of indoor and subterranean environments.
TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics
Transformer neural networks are rapidly being integrated into state-of-the-art solutions for natural language processing (NLP) and computer vision.
Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing
Emerging AI applications such as ChatGPT, graph convolutional networks, and other deep neural networks require massive computational resources for training and inference.
MOELA: A Multi-Objective Evolutionary/Learning Design Space Exploration Framework for 3D Heterogeneous Manycore Platforms
To enable emerging applications such as deep machine learning and graph processing, 3D network-on-chip (NoC) enabled heterogeneous manycore platforms that can integrate many processing elements (PEs) are needed.
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms
Machine learning (ML) algorithms are increasingly being integrated into embedded and IoT systems that surround us, and they are vulnerable to adversarial attacks.
R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning
In this paper, we introduce a novel semi-structured pruning framework called R-TOSS that overcomes the shortcomings of state-of-the-art model pruning techniques.
VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization
Wi-Fi fingerprinting-based indoor localization is an emerging embedded application domain that leverages existing Wi-Fi access points (APs) in buildings to localize users with smartphones.
Machine Learning Accelerators in 2.5D Chiplet Platforms with Silicon Photonics
Domain-specific machine learning (ML) accelerators such as Google's TPU and Apple's Neural Engine now dominate CPUs and GPUs for energy-efficient ML processing.
Ethical Design of Computers: From Semiconductors to IoT and Artificial Intelligence
Computing systems are tightly integrated today into our professional, social, and private lives.
AI Ethics in Smart Healthcare
This article reviews the landscape of ethical challenges of integrating artificial intelligence (AI) into smart healthcare products, including medical electronic devices.
Ethics for Digital Medicine: A Path for Ethical Emerging Medical IoT Design
The dawn of the digital medicine era, ushered in by increasingly powerful embedded systems and Internet of Things (IoT) computing devices, is creating new therapies and biomedical solutions that promise to positively transform our quality of life.
RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics
Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data sequences, such as speech recognition, human activity recognition, and anomaly detection.
RACE: A Reinforcement Learning Framework for Improved Adaptive Control of NoC Channel Buffers
Network-on-chip (NoC) architectures rely on buffers to store flits to cope with contention for router resources during packet switching.
A Framework for CSI-Based Indoor Localization with 1D Convolutional Neural Networks
Modern indoor localization techniques are essential to overcome the weak GPS coverage in indoor environments.
Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization
One such critical challenge is device heterogeneity, i. e., the variation in the RSSI signal characteristics captured across different smartphone devices.
A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management
We examine the challenges and the issues in current research focused on ML for cloud management and explore strategies for addressing these issues.
A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization
Parameter quantization in convolutional neural networks (CNNs) can help generate efficient models with lower memory footprint and computational complexity.
Robust Perception Architecture Design for Automotive Cyber-Physical Systems
In emerging automotive cyber-physical systems (CPS), accurate environmental perception is critical to achieving safety and performance goals.
Characterization and Optimization of Integrated Silicon-Photonic Neural Networks under Fabrication-Process Variations
Simulation results for two example SPNNs of different scales under realistic and correlated FPVs indicate that the optimized MZIs can improve the inferencing accuracy by up to 93. 95% for the MNIST handwritten digit dataset -- considered as an example in this paper -- which corresponds to a <0. 5% accuracy loss compared to the variation-free case.
LoCI: An Analysis of the Impact of Optical Loss and Crosstalk Noise in Integrated Silicon-Photonic Neural Networks
Compared to electronic accelerators, integrated silicon-photonic neural networks (SP-NNs) promise higher speed and energy efficiency for emerging artificial-intelligence applications.
Object Detection in Autonomous Vehicles: Status and Open Challenges
Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans.
Roadmap for Cybersecurity in Autonomous Vehicles
Autonomous vehicles are on the horizon and will be transforming transportation safety and comfort.
Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis
Singular-value-decomposition-based coherent integrated photonic neural networks (SC-IPNNs) have a large footprint, suffer from high static power consumption for training and inference, and cannot be pruned using conventional DNN pruning techniques.
CHAMP: Coherent Hardware-Aware Magnitude Pruning of Integrated Photonic Neural Networks
We propose a novel hardware-aware magnitude pruning technique for coherent photonic neural networks.
Siamese Neural Encoders for Long-Term Indoor Localization with Mobile Devices
These factors are often ignored in indoor localization frameworks and cause gradual and catastrophic degradation of localization accuracy post-deployment (over weeks and months).
SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning
Sparse neural networks can greatly facilitate the deployment of neural networks on resource-constrained platforms as they offer compact model sizes while retaining inference accuracy.
ROBIN: A Robust Optical Binary Neural Network Accelerator
However, mapping sophisticated neural network models on these accelerators still entails significant energy and memory consumption, along with high inference time overhead.
LATTE: LSTM Self-Attention based Anomaly Detection in Embedded Automotive Platforms
Our proposed LATTE framework uses a stacked Long Short Term Memory (LSTM) predictor network with novel attention mechanisms to learn the normal operating behavior at design time.
CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning
GPS technology has revolutionized the way we localize and navigate outdoors.
QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices
Indoor localization services are a crucial aspect for the realization of smart cyber-physical systems within cities of the future.
CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator
Domain-specific neural network accelerators have seen growing interest in recent years due to their improved energy efficiency and inference performance compared to CPUs and GPUs.
A Survey of Resource Management for Processing-in-Memory and Near-Memory Processing Architectures
Due to amount of data involved in emerging deep learning and big data applications, operations related to data movement have quickly become the bottleneck.
Hardware Architecture
